Defect inspection in manufacturing is conducting inspection during the production process. This approach of inspection helps control the quality of products by fixing the sources of defects immediately after they are detected, and it is useful for any factory that wants to improve productivity, reduce defect rates, and reduce re-work and waste. The measurement of defect information is usually conducted on component texture information or height information. In conventional methods, we usually measure and consider the local defect distribution to determine if the area has defects or not.
Many advanced image processing applications call for estimating the spatial density of blob in a binary image. One of the applications is to remove the noise blobs from the defect blobs in defect inspection. In such case, the blobs region with low density will be considered as noise and other regions will be considered as defects. By using this technology, the user can easily distinguish the defect regions from a low contrast and rough surface. Along these lines, automatic optical inspection (AOI) for rough surface can be realized.
However, the density estimation is always almost based on window searching algorithms, in which the algorithm need to do a full searching to estimate the density for each spatial location (that the pixel location). For instance, for an image of W×H size, the complexity of the existing searching based algorithm is O(W*H*ε2), wherein ε2 is the size of the neighborhood which may be the searching window, O represents computation complexity. We can find that the complexity increases dramatically when ε2 increase (2 order of increasing magnitude). In vision inspection application with high precision, the window size is always large, e.g. 50×50, which is the corresponding complexity will be 100 times larger than in the case of 5×5. Along these lines, the blob density estimation will be slowed down and the high speed inspection will suffer from this low speed estimation algorithm.
There is a need in the art to have a high speed spatial density estimation algorithm to estimate defect density maps for binary large object (blob) analysis in image processing field for inspection.